In this work, we focus on Model Agnostic Meta-Learning (MAML) [1]}, which is an optimization-based meta-learning method.
To efficiently adapt to a task training data, MAML jointly optimizes the initial point of a neural network model and a gradient-descent based optimizer.
This is framed as a bi-level optimization consisting of an inner loop for task-specific learning and outer loop for fast adaptation across tasks:
\(&\mbox{Inner: } \theta ^{\prime }_i \leftarrow \theta - \alpha \nabla _{\theta } \mathcal {L}_i(\mathcal {D}^{tr}, \theta ) \\&\mbox{Outer: } \Theta \leftarrow \Theta - \beta \; \nabla _{\Theta } \mathbb {E}_{T_i \sim \mathcal {P}(\mathcal {T})} \left[ L_i(\mathcal {D}^{val}, \theta ^{\prime }_i) \right] &\) 
